Building Damage Assessment
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Benchmarks
BRIGHT
Most implemented
Building Damage Annotation on Post-Hurricane Satellite Imagery Based on Convolutional Neural Networks
xBD: A Dataset for Assessing Building Damage from Satellite Imagery
Large-scale Building Damage Assessment using a Novel Hierarchical Transformer Architecture on Satellite Images
Flood-DamageSense: Multimodal Mamba with Multitask Learning for Building Flood Damage Assessment using SAR Remote Sensing Imagery
Papers
Quantum-Grassmann-Plucker Token Mixing for Deep Learning-Based Post-Disaster Damage Assessment
Timely post-disaster building damage assessment from satellite imagery is a critical engineering decision support task, yet it remains constrained by class imbalance, ambiguous intermediate damage states, and limited cro…
Building Damage AssessmentQuantum Machine LearningImage ClassificationGeBDA: Building Damage Assessment as Text-Based Sequence Prediction
Conventionally, Building Damage Assessment (BDA) is tackled either with dedicated network architectures or by fine-tuning geospatial image foundation models. In this work, we ask whether a general-purpose Vision-Language…
Building Damage AssessmentDamage-TriageFormer: A Foundation-Model Framework for Typology-Based Building Damage Assessment from Mono-Temporal Imagery
Decision-relevant building damage assessment is critical for prioritizing resources and recovery after a disaster, yet most automated methods either flatten damage into a single severity scale (no damage, minor, major, d…
Building Damage AssessmentOptimizing Latent Representations for Robust Building Damage Assessment Onboard Earth Observation Satellites
Rapid identification of damaged buildings after natural disasters or on war areas is crucial to support emergency response and prioritize interventions. Earth Observation constellations provide timely, large-scale covera…
Building Damage AssessmentData AugmentationFrom Pixels to Semantics: A Multi-Stage AI Framework for Structural Damage Detection in Satellite Imagery
Rapid and accurate structural damage assessment following natural disasters is critical for effective emergency response and recovery. However, remote sensing imagery often suffers from low spatial resolution, contextual…
Building Damage AssessmentVideo RestorationObject DetectionDecision MakingTornadoNet: Real-Time Building Damage Detection with Ordinal Supervision
We present TornadoNet, a comprehensive benchmark for automated street-level building damage assessment evaluating how modern real-time object detection architectures and ordinal-aware supervision strategies perform under…
Building Damage AssessmentReal-Time Object DetectionOrdinal Classification